Efficient use of wind-generated electrical energy can be assisted by accurate and sufficiently-early electrical power output forecasts for wind farms and regions of wind farms. Current forecasting methods do not give much advanced warning and are prone to error when there are substantial changes in power output. In other words, current forecasting methods can be ineffective when wind speeds and direction vary from established trends. The inability to sufficiently predict dynamics in electrical power output is in part due to the traditional use of numerical weather models, which are computationally-intensive and thus do not model weather patterns fast enough to handle wind dynamics.
In particular, wind speed data is typically entered into a numerical weather model in order to predict future wind speeds. The predicted wind speeds are then converted to an estimated electrical power output for each wind turbine in a given location or region by passing the data through a power curve tailored to the given wind turbines. However, the modeling is time-consuming and thus predictions tend to be inaccurate when predicting large changes in regional wind power output, especially on lead times shorter than 12 hours.
Accuracy is also hampered by the inability to obtain wind speed data at the location of every wind turbine unless one owns the wind turbines. Given the limited locations that can be used to place wind speed sensors, typical electrical power output methods are fraught with the inaccuracies of predicting wind speeds at locations other than where the sensors are located.